摘要
提出了一种并行地图匹配方法,高效处理海量浮动车流数据。该方法顾及交通网络拓扑,指出网格过滤、距离过滤和方向过滤等策略减少邻近候选节点的数量,利用预先生成的最短路径列表减少最短路径计算量。基于非关系型分布式数据库实现了高效率的浮动车流数据并行地图匹配,利用武汉市的浮动车流数据进行了实验。实验结果表明,本文方法正确率为90.6%,计算效率能满足大规模浮动车流数据实时处理的需要。
Mp-matching floating car data is a fundamental task in traffic surveillance, traffic anomaly detection, and urban dynamic analysis. This study proposes a parallel map-matching approach to process streaming large volume floating car data. Considering the connectivity of a transportation net- work, the matching candidates are limited with a coarse spatial grid. A distance filter and a direction filter are combined to reduce the number of matching candidates. The trajectory between consecutive nodes is recovered with a shortest path list. The shortest path list in memory was developed to reduce the computation and speed up the matching process. A non-relational distributed database parallelizes the map-matching procedure. The performance of the presented approach was tested with large vol- ume floating car data in Wuhan, China. It demonstrates that this method achieves 90.62% correct map-matching results. This efficiency could fulfill the needs of real-time traffic monitoring, and will benefit trajectory analysis.
出处
《武汉大学学报(信息科学版)》
EI
CSCD
北大核心
2017年第5期697-703,共7页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金(41401444
41371377)
深圳市战略性新兴产业发展专项资金(JCYJ20121019111128765)
深圳市基础研究计划(JCYJ20140828163633980)
中国博士后科学基金面上项目(2014M560671)
测绘遥感信息工程国家重点实验室开放基金(13S02)~~